Signature Activation: A Sparse Signal View for Holistic Saliency

• URL: http://arxiv.org/abs/2309.11443v1
• Date: Wed, 20 Sep 2023 16:17:26 GMT
• Title: Signature Activation: A Sparse Signal View for Holistic Saliency
• Authors: Jose Roberto Tello Ayala, Akl C. Fahed, Weiwei Pan, Eugene V. Pomerantsev, Patrick T. Ellinor, Anthony Philippakis, Finale Doshi-Velez
• Abstract summary: We introduce Signature Activation, a saliency method that generates holistic and class-agnostic explanations for CNN outputs. Our method exploits the fact that certain kinds of medical images, such as angiograms, have clear foreground and background objects. We show the potential use of our method in clinical settings through evaluating its efficacy for aiding the detection of lesions in coronary angiograms.
• Score: 18.699129959911485
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The adoption of machine learning in healthcare calls for model transparency and explainability. In this work, we introduce Signature Activation, a saliency method that generates holistic and class-agnostic explanations for Convolutional Neural Network (CNN) outputs. Our method exploits the fact that certain kinds of medical images, such as angiograms, have clear foreground and background objects. We give theoretical explanation to justify our methods. We show the potential use of our method in clinical settings through evaluating its efficacy for aiding the detection of lesions in coronary angiograms.

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